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transformers/docs/source/en/model_doc/mbart.md
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transformers/docs/source/en/model_doc/mbart.md
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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*This model was released on 2020-01-22 and added to Hugging Face Transformers on 2020-11-16.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# mBART
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[mBART](https://huggingface.co/papers/2001.08210) is a multilingual machine translation model that pretrains the entire translation model (encoder-decoder) unlike previous methods that only focused on parts of the model. The model is trained on a denoising objective which reconstructs the corrupted text. This allows mBART to handle the source language and the target text to translate to.
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[mBART-50](https://huggingface.co/paper/2008.00401) is pretrained on an additional 25 languages.
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You can find all the original mBART checkpoints under the [AI at Meta](https://huggingface.co/facebook?search_models=mbart) organization.
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> [!TIP]
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> Click on the mBART models in the right sidebar for more examples of applying mBART to different language tasks.
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> [!NOTE]
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> The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`
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The example below demonstrates how to translate text with [`Pipeline`] or the [`AutoModel`] class.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="translation",
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model="facebook/mbart-large-50-many-to-many-mmt",
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device=0,
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dtype=torch.float16,
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src_lang="en_XX",
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tgt_lang="fr_XX",
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)
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print(pipeline("UN Chief Says There Is No Military Solution in Syria"))
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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article_en = "UN Chief Says There Is No Military Solution in Syria"
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model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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tokenizer.src_lang = "en_XX"
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encoded_hi = tokenizer(article_en, return_tensors="pt").to(model.device)
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generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"], cache_implementation="static")
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print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True))
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```
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</hfoption>
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</hfoptions>
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## Notes
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- You can check the full list of language codes via `tokenizer.lang_code_to_id.keys()`.
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- mBART requires a special language id token in the source and target text during training. The source text format is `X [eos, src_lang_code]` where `X` is the source text. The target text format is `[tgt_lang_code] X [eos]`. The `bos` token is never used. The [`~PreTrainedTokenizerBase._call_`] encodes the source text format passed as the first argument or with the `text` keyword. The target text format is passed with the `text_label` keyword.
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- Set the `decoder_start_token_id` to the target language id for mBART.
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```py
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import torch
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-en-ro", dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
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tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX")
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article = "UN Chief Says There Is No Military Solution in Syria"
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inputs = tokenizer(article, return_tensors="pt")
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translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
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tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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```
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- mBART-50 has a different text format. The language id token is used as the prefix for the source and target text. The text format is `[lang_code] X [eos]` where `lang_code` is the source language id for the source text and target language id for the target text. `X` is the source or target text respectively.
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- Set the `eos_token_id` as the `decoder_start_token_id` for mBART-50. The target language id is used as the first generated token by passing `forced_bos_token_id` to [`~GenerationMixin.generate`].
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```py
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import torch
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
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tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
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tokenizer.src_lang = "ar_AR"
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encoded_ar = tokenizer(article_ar, return_tensors="pt")
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generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
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tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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```
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## MBartConfig
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[[autodoc]] MBartConfig
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## MBartTokenizer
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[[autodoc]] MBartTokenizer
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- build_inputs_with_special_tokens
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## MBartTokenizerFast
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[[autodoc]] MBartTokenizerFast
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## MBart50Tokenizer
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[[autodoc]] MBart50Tokenizer
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## MBart50TokenizerFast
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[[autodoc]] MBart50TokenizerFast
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## MBartModel
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[[autodoc]] MBartModel
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## MBartForConditionalGeneration
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[[autodoc]] MBartForConditionalGeneration
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## MBartForQuestionAnswering
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[[autodoc]] MBartForQuestionAnswering
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## MBartForSequenceClassification
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[[autodoc]] MBartForSequenceClassification
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## MBartForCausalLM
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[[autodoc]] MBartForCausalLM
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- forward
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